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Robust mixed model analysis[electron...

Jiang, Jiming.

Robust mixed model analysis[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	519.5/36
書名/作者:	Robust mixed model analysis/ Jiming Jiang.
作者:	Jiang, Jiming.
出版者:	Singapore : : World Scientific Publishing,, c2019.
面頁冊數:	1 online resource (268 p.) : : ill.
標題:	Multilevel models (Statistics)
標題:	Linear models (Statistics)
標題:	Mathematical models
ISBN:	9789814733847
書目註:	Includes bibliographical references (p. 243-252) and index.
摘要、提要註:	"Mixed-effects models have found broad applications in various fields. As a result, the interest in learning and using these models is rapidly growing. On the other hand, some of these models, such as the linear mixed models and generalized linear mixed models, are highly parametric, involving distributional assumptions that may not be satisfied in real-life problems. Therefore, it is important, from a practical standpoint, that the methods of inference about these models are robust to violation of model assumptions. Fortunately, there is a full scale of methods currently available that are robust in certain aspects. Learning about these methods is essential for the practice of mixed-effects models. This research monograph provides a comprehensive account of methods of mixed model analysis that are robust in various aspects, such as to violation of model assumptions, or to outliers. It is suitable as a reference book for a practitioner who uses the mixed-effects models, and a researcher who studies these models. It can also be treated as a graduate text for a course on mixed-effects models and their applications."--
電子資源:	https://www.worldscientific.com/worldscibooks/10.1142/9888#t=toc

Robust mixed model analysis[electronic resource] /
Jiang, Jiming.

Robust mixed model analysis[electronic resource] /Jiming Jiang. - 1st ed. - Singapore :World Scientific Publishing,c2019. - 1 online resource (268 p.) :ill.

Includes bibliographical references (p. 243-252) and index.

"Mixed-effects models have found broad applications in various fields. As a result, the interest in learning and using these models is rapidly growing. On the other hand, some of these models, such as the linear mixed models and generalized linear mixed models, are highly parametric, involving distributional assumptions that may not be satisfied in real-life problems. Therefore, it is important, from a practical standpoint, that the methods of inference about these models are robust to violation of model assumptions. Fortunately, there is a full scale of methods currently available that are robust in certain aspects. Learning about these methods is essential for the practice of mixed-effects models. This research monograph provides a comprehensive account of methods of mixed model analysis that are robust in various aspects, such as to violation of model assumptions, or to outliers. It is suitable as a reference book for a practitioner who uses the mixed-effects models, and a researcher who studies these models. It can also be treated as a graduate text for a course on mixed-effects models and their applications."--

ISBN: 9789814733847Subjects--Topical Terms:

340208
Multilevel models (Statistics)

LC Class. No.: QA278 / .J53 2019

Dewey Class. No.: 519.5/36

Robust mixed model analysis[electronic resource] /
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245 1 0 $a Robust mixed model analysis $h [electronic resource] / $c Jiming Jiang.
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260 $a Singapore : $b World Scientific Publishing, $c c2019.
300 $a 1 online resource (268 p.) : $b ill.
504 $a Includes bibliographical references (p. 243-252) and index.
520 $a "Mixed-effects models have found broad applications in various fields. As a result, the interest in learning and using these models is rapidly growing. On the other hand, some of these models, such as the linear mixed models and generalized linear mixed models, are highly parametric, involving distributional assumptions that may not be satisfied in real-life problems. Therefore, it is important, from a practical standpoint, that the methods of inference about these models are robust to violation of model assumptions. Fortunately, there is a full scale of methods currently available that are robust in certain aspects. Learning about these methods is essential for the practice of mixed-effects models. This research monograph provides a comprehensive account of methods of mixed model analysis that are robust in various aspects, such as to violation of model assumptions, or to outliers. It is suitable as a reference book for a practitioner who uses the mixed-effects models, and a researcher who studies these models. It can also be treated as a graduate text for a course on mixed-effects models and their applications."-- $c Publisher's website.
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650 0 $a Linear models (Statistics) $3 224081
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856 4 0 $u https://www.worldscientific.com/worldscibooks/10.1142/9888#t=toc

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